In [1]:
import pandas as pd
from Schedule import Schedule
//anaconda/lib/python2.7/site-packages/pandas/computation/__init__.py:19: UserWarning: The installed version of numexpr 2.4.4 is not supported in pandas and will be not be used
UserWarning)
In [2]:
sched = Schedule('1/1/2015', '1/3/2015')
In [3]:
sched.games.columns
Out[3]:
Index([u'GAME_DATE', u'H_WL', u'A_WL', u'Home Team', u'Away Team',
u'H_PTS', u'A_PTS', u'Pts_diff', u'FGM_home', u'FG3M_home',
u'FGA_home', u'OREB_home', u'DREB_away', u'TOV_home', u'FTM_home',
u'FTA_home', u'FGM_away', u'FG3M_away', u'FGA_away', u'OREB_away',
u'DREB_home', u'TOV_away', u'FTM_away', u'FTA_away', u'H_AST',
u'A_AST', u'H_STL', u'A_STL', u'H_BLK', u'A_BLK'],
dtype='object')
In [4]:
sched.games
Out[4]:
GAME_DATE
H_WL
A_WL
Home Team
Away Team
H_PTS
A_PTS
Pts_diff
FGM_home
FG3M_home
...
DREB_home
TOV_away
FTM_away
FTA_away
H_AST
A_AST
H_STL
A_STL
H_BLK
A_BLK
64
2015-01-02
0
1
BOS
DAL
101
119
-18
38
12
...
29
11
16
17
22
24
6
8
2
1
149
2015-01-02
1
0
NOP
HOU
111
83
28
44
7
...
37
19
7
11
24
22
12
13
8
3
189
2015-01-03
1
0
CHI
BOS
109
104
5
37
6
...
35
20
9
11
19
26
10
9
9
6
190
2015-01-01
1
0
CHI
DEN
106
101
5
38
8
...
33
12
22
27
22
19
7
4
18
7
269
2015-01-03
1
0
DEN
MEM
114
85
29
41
7
...
40
12
9
13
21
20
6
6
8
8
314
2015-01-02
1
0
GSW
TOR
126
105
21
49
12
...
31
15
15
20
35
23
8
7
8
1
351
2015-01-03
1
0
HOU
MIA
115
79
36
41
13
...
31
21
15
25
21
20
13
6
1
3
391
2015-01-03
1
0
LAC
PHI
127
91
36
46
15
...
34
21
18
26
33
17
13
7
3
1
435
2015-01-02
0
1
LAL
MEM
106
109
-3
43
6
...
33
12
21
31
24
27
10
7
8
4
519
2015-01-02
0
1
MIL
IND
91
94
-3
38
7
...
33
15
16
20
28
24
10
7
7
3
558
2015-01-03
0
1
MIN
UTA
89
101
-12
34
6
...
23
12
14
21
17
23
8
6
5
10
559
2015-01-01
0
1
MIN
SAC
107
110
-3
42
7
...
23
20
25
29
22
23
12
7
6
7
639
2015-01-02
0
1
NYK
DET
81
97
-16
32
10
...
30
16
8
14
18
24
13
10
4
3
681
2015-01-03
0
1
ORL
CHA
90
98
-8
33
4
...
32
14
24
32
18
16
9
4
3
4
682
2015-01-02
0
1
ORL
BKN
98
100
-2
37
11
...
29
22
11
18
22
26
13
7
3
5
805
2015-01-02
1
0
PHX
PHI
112
96
16
42
14
...
35
16
15
25
24
20
9
12
12
7
843
2015-01-03
0
1
POR
ATL
107
115
-8
42
13
...
33
14
23
31
24
17
6
9
7
5
926
2015-01-03
1
0
SAS
WAS
101
92
9
43
4
...
29
6
8
13
27
23
4
6
5
6
967
2015-01-02
1
0
OKC
WAS
109
102
7
44
8
...
32
15
13
15
21
27
8
3
4
1
1170
2015-01-02
0
1
CHA
CLE
87
91
-4
32
6
...
35
7
21
30
20
16
5
8
6
5
1214
2015-01-02
0
1
UTA
ATL
92
98
-6
32
10
...
30
10
26
30
18
24
6
9
9
3
21 rows × 30 columns
In [5]:
sched.add_four_factors()
Out[5]:
GAME_DATE
H_WL
A_WL
Home Team
Away Team
H_PTS
A_PTS
Pts_diff
FGM_home
FG3M_home
...
H_BLK
A_BLK
H_FF_EFG
H_FF_ORB
H_FF_FTFGA
H_FF_TOV
A_FF_EFG
A_FF_ORB
A_FF_FTFGA
A_FF_TOV
64
2015-01-02
0
1
BOS
DAL
101
119
-18
38
12
...
2
1
0.494382
0.200000
0.146067
0.119570
0.536458
0.355556
0.166667
0.111698
149
2015-01-02
1
0
NOP
HOU
111
83
28
44
7
...
8
3
0.572289
0.324324
0.192771
0.184049
0.431818
0.260000
0.079545
0.192230
189
2015-01-03
1
0
CHI
BOS
109
104
5
37
6
...
9
6
0.400000
0.406780
0.290000
0.145985
0.475000
0.222222
0.090000
0.174155
190
2015-01-01
1
0
CHI
DEN
106
101
5
38
8
...
18
7
0.461538
0.245283
0.241758
0.072917
0.429348
0.340000
0.239130
0.121359
269
2015-01-03
1
0
DEN
MEM
114
85
29
41
7
...
8
8
0.563291
0.216216
0.316456
0.133142
0.441860
0.166667
0.104651
0.125366
314
2015-01-02
1
0
GSW
TOR
126
105
21
49
12
...
8
1
0.597826
0.350000
0.173913
0.093946
0.523256
0.261905
0.174419
0.151822
351
2015-01-03
1
0
HOU
MIA
115
79
36
41
13
...
1
3
0.572289
0.404762
0.240964
0.169635
0.470588
0.205128
0.220588
0.228261
391
2015-01-03
1
0
LAC
PHI
127
91
36
46
15
...
3
1
0.629412
0.230769
0.235294
0.083822
0.462025
0.306122
0.227848
0.217752
435
2015-01-02
0
1
LAL
MEM
106
109
-3
43
6
...
8
4
0.567901
0.131579
0.172840
0.133525
0.523810
0.282609
0.250000
0.124172
519
2015-01-02
0
1
MIL
IND
91
94
-3
38
7
...
7
3
0.466292
0.222222
0.089888
0.125786
0.481481
0.232558
0.197531
0.158228
558
2015-01-03
0
1
MIN
UTA
89
101
-12
34
6
...
5
10
0.430233
0.280000
0.174419
0.090992
0.580000
0.323529
0.186667
0.140779
559
2015-01-01
0
1
MIN
SAC
107
110
-3
42
7
...
6
7
0.500000
0.319149
0.175824
0.140449
0.574324
0.303030
0.337838
0.206697
639
2015-01-02
0
1
NYK
DET
81
97
-16
32
10
...
4
3
0.406593
0.298246
0.076923
0.120667
0.585526
0.230769
0.105263
0.179453
681
2015-01-03
0
1
ORL
CHA
90
98
-8
33
4
...
3
4
0.402299
0.244898
0.229885
0.076721
0.474359
0.333333
0.307692
0.155417
682
2015-01-02
0
1
ORL
BKN
98
100
-2
37
11
...
3
5
0.482955
0.163265
0.147727
0.110220
0.618056
0.121212
0.152778
0.224673
805
2015-01-02
1
0
PHX
PHI
112
96
16
42
14
...
12
7
0.583333
0.342105
0.166667
0.195796
0.465517
0.313725
0.172414
0.163265
843
2015-01-03
0
1
POR
ATL
107
115
-8
42
13
...
7
5
0.521505
0.234043
0.107527
0.170973
0.547619
0.232558
0.273810
0.137741
926
2015-01-03
1
0
SAS
WAS
101
92
9
43
4
...
5
6
0.576923
0.290323
0.141026
0.115048
0.512195
0.236842
0.097561
0.070822
967
2015-01-02
1
0
OKC
WAS
109
102
7
44
8
...
4
1
0.578313
0.162162
0.156627
0.104866
0.529762
0.200000
0.154762
0.153689
1170
2015-01-02
0
1
CHA
CLE
87
91
-4
32
6
...
6
5
0.402299
0.312500
0.195402
0.143678
0.402299
0.270833
0.241379
0.074310
1214
2015-01-02
0
1
UTA
ATL
92
98
-6
32
10
...
9
3
0.430233
0.320755
0.209302
0.149637
0.444444
0.268293
0.320988
0.107296
21 rows × 38 columns
In [8]:
d1=pd.to_datetime('1/1/2001')
d2=pd.to_datetime('1/2/2001')
(d2-d1).days
Out[8]:
1
In [ ]:
Content source: mprego/NBA
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